Potato is the largest non-cereal food crop in the world. Timely estimation of end-of-season tuber production using in-season information can inform sustainable agricultural management decisions that increase productivity while reducing impacts on the environment. Recently, unmanned aerial vehicles (UAVs) have become increasingly popular in precision agriculture due to their flexibility in data acquisition and improved spatial and spectral resolutions. In addition, compared with natural color and multispectral imagery, hyperspectral data can provide higher spectral fidelity which is important for modelling crop traits. In this study, we conducted end-of-season potato tuber yield and tuber set predictions using in-season UAV-based hyperspectral images and machine learning. Specifically, six mainstream machine learning models, i.e., ordinary least square (OLS), ridge regression, partial least square regression (PLSR), support vector regression (SVR), random forest (RF), and adaptive boosting (AdaBoost), were developed and compared across potato research plots with different irrigation rates at the University of Wisconsin Hancock Agricultural Research Station. Our results showed that the tuber set could be better predicted than the tuber yield, and using the multi-temporal hyperspectral data improved the model performance. Ridge achieved the best performance for predicting tuber yield (R² = 0.63) while Ridge and PLSR had similar performance for predicting tuber set (R² = 0.69). Our study demonstrated that hyperspectral imagery and machine learning have good potential to help potato growers efficiently manage their irrigation practices.

中文翻译：

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使用基于季节无人机的高光谱图像和机器学习预测马铃薯的季末块茎产量和块茎集。

马铃薯是世界上最大的非谷物粮食作物。利用季节信息及时估算季节末的块茎产量，可以为可持续农业管理决策提供依据，从而提高生产力，同时减少对环境的影响。近年来，由于无人机在数据采集中的灵活性以及改进的空间和光谱分辨率，无人机已在精密农业中变得越来越流行。此外，与自然色彩和多光谱图像相比，高光谱数据可以提供更高的光谱保真度，这对于建模农作物性状非常重要。在这项研究中，我们使用基于无人机的季节性高光谱图像和机器学习进行了季末马铃薯块茎产量和块茎预测。具体来说，有六种主流机器学习模型，即 在不同灌溉率的马铃薯研究区中开发并比较了普通最小二乘（OLS），岭回归，偏最小二乘回归（PLSR），支持向量回归（SVR），随机森林（RF）和自适应增强（AdaBoost）在威斯康星大学汉考克大学农业研究站。我们的结果表明，与块茎产量相比，可以更好地预测块茎集，并且使用多时相高光谱数据可以提高模型性能。里奇（Ridge）在预测块茎产量（R 在威斯康星大学汉考克大学农业研究站，以不同灌溉速率在马铃薯研究区开发并比较了这些作物。我们的结果表明，与块茎产量相比，块茎集可以得到更好的预测，并且使用多时相高光谱数据可以提高模型性能。里奇（Ridge）在预测块茎产量（R 在威斯康星大学汉考克大学农业研究站，以不同灌溉率在马铃薯研究区进行了开发和比较。我们的结果表明，与块茎产量相比，可以更好地预测块茎集，并且使用多时相高光谱数据可以提高模型性能。里奇（Ridge）在预测块茎产量（R² = 0.63），而Ridge和PLSR在预测块茎集结方面具有相似的表现（R ² = 0.69）。我们的研究表明，高光谱图像和机器学习具有帮助马铃薯种植者有效管理其灌溉实践的良好潜力。